Abstract
Few-shot learning (FSL) approaches, mostly neural network-based, assume that pre-trained knowledge can be obtained from base (seen) classes and transferred to novel (unseen) classes. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper FSL application in some risk-sensitive areas. In this paper, we reveal a new way to perform FSL for image classification, using a visual representation from the backbone model and patterns generated by a self-attention based explainable module. The representation weighted by patterns only includes a minimum number of distinguishable features and the visualized patterns can serve as an informative hint on the transferred knowledge. On three mainstream datasets, experimental results prove that the proposed method can enable satisfying explainability and achieve high classification results. Code is available at https://github.com/wbw520/MTUNet.
Bowen Wang
Specially-Appointed Researcher/Fellow
Liangzhi Li
Guest Assistant Professor
His research interests lie in deep learning, computer vision, robotics, and medical images.
Manisha Verma
Specially-Appointed Researcher/Fellow
Manisha’s research interest broadly lies in computer vision and image processing. Currently, she is working on micro facial expression recognition using multi-model deep learning frameworks.
Yuta Nakashima
Professor
Yuta Nakashima is a professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.
Hajime Nagahara
Professor
He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.